Numerical and Experimental Analysis of the Shear Behavior of Ultrahigh-Performance Concrete Construction Joints

Shear performance of plain UHPC (ultrahigh-performance concrete) construction joints is studied in both experimental and analytical ways. In push-off tests, three different contact surfaces of the construction joint were considered, while the case without any joint was provided for the reference. Test results indicate that the geometry of contact surfaces greatly affects shear performance of the construction joint. With simplifying structural behavior of contact surfaces and UHPC substrate, the finite-element analysis model is developed for every case studied by utilizing the ABAQUS software and validated against the test results. Agreement between experimental and numerical simulation results is excellent especially in terms of displacement, strength, and failure mechanism. It is expected that the present work provides a basis for further study on reinforced UHPC construction joints

Synaptic Transistor Based on In‐Ga‐Zn‐O Channel and Trap Layers with Highly Linear Conductance Modulation for Neuromorphic Computing

Abstract Brain‐inspired neuromorphic computing has drawn significant attraction as a promising technology beyond von Neumann architecture by using the parallel structure of synapses and neurons. Various artificial synapse configurations and materials have been proposed to emulate synaptic behaviors for human brain functions such as memorizing, learning, and visual processing. Especially, the memory type indium‐gallium‐zinc‐oxide (IGZO) synaptic transistor adopting a charge trapping layer (CTL) has the advantages of high stability and a low leakage current of the IGZO channel. However, the CTL material should be carefully selected and optimized to overcome the low de‐trapping efficiency, resulting from difficulty in inducing holes in the IGZO channel. In this paper, IGZO is adopted as a CTL and found out that making it degenerated is crucial to improving de‐trapping efficiency. The degenerate CTL, where electrons remain as free electrons, induces Fowler‐Nordheim tunneling by increasing the electric field across the tunneling layer. As a result, the synaptic transistor represents a high linearity of potentiation (αp: −0.03) and depression (αd: −0.47) with 64 conductance levels, which enables the spiking neural network simulation to achieve high accuracy of 98.08%. These experimental results indicate that the synapse transistor can be one of the promising candidates for neuromorphic applications

Large-scale financial planning via a partially observable stochastic dual dynamic programming framework

The multi-stage stochastic programming (MSP) approach is widely used to solve financial planning problems owing to its flexibility. However, the size of an MSP problem grows exponentially with the number of stages, and such problem can easily become computationally intractable. Financial planning problems often consider planning horizons of several decades, and thus, the curse of dimensionality can become a critical issue. Stochastic dual dynamic programming (SDDP), a sampling-based decomposition algorithm, has emerged to resolve this issue. While SDDP has been successfully implemented in the energy domain, few applications of SDDP are found in the finance domain. In this study, we identify the major obstacle in using SDDP to solve financial planning problems to be the stagewise independence assumption and propose a partially observable SDDP (PO-SDDP) framework to overcome such limitations. We argue that the PO-SDDP framework, which models uncertainties using discrete-valued partially observable Markov states and introduces feasibility cuts, can properly address large-scale financial planning problems

Chatbot with Touch and Graphics: An Interaction of Users for Emotional Expression and Turn-taking

Use of chatbots for emotional exchange is recently increasing in various domains. However, as existing chatbots have been considered in terms of natural language processing techniques for interaction with text-based chatting, chatbot interaction with users is lacking in terms of considering the emotions of users and managing turn-taking in conversation. This paper suggests an interaction technique having touch interactions with graphic interfaces (TwG) to solve these problems. In the system, users send their emotions and manage turn-taking through TwG technique. We conducted a Wizard of Oz study to evaluate user experience on emotional expression and turn-taking with TwG technique. Results showed that TwG interaction improved emotional expression compared to a traditional text-based chatbot interaction. Furthermore, the results showed that TwG positively affects natural turn-taking of the conversation

Kohn-Sham Time-Dependent Density Functional Theory on the Massively Parallel Graphics Processing Units

We report a high-performance multi graphics processing unit (GPU) implementation of the Kohn-Sham time-dependent density functional theory (TDDFT) within the Tamm-Dancoff approximation. Our newly developed GPU algorithm on massively parallel computing systems using multiple parallel models in tandem scales optimally with material size, considerably reducing the computational wall time. A benchmark TDDFT study was performed on a green fluorescent protein complex composed of 4,353 atoms with 40,518 atomic orbitals represented by Gaussian-type functions. As the largest molecule attempted to date to the best of our knowledge, the proposed strategy demonstrated reasonably high efficiencies up to 256 GPUs on a custom-built state-of-the-art GPU computing system with Nvidia A100 GPUs. We believe that our GPU-oriented algorithms, which empower first-principles simulation for very large-scale applications, may render deeper understanding of the molecular basis of material behaviors, eventually revealing new possibilities for breakthrough designs on new material systems